6131271
1
Jan van Rijn
9954
Supervised Classification
7096
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier))(1)
4163297
bootstrap
true
6902
class_weight
null
6902
criterion
"entropy"
6902
max_depth
null
6902
max_features
"auto"
6902
max_leaf_nodes
null
6902
min_impurity_split
1e-07
6902
min_samples_leaf
9
6902
min_samples_split
12
6902
min_weight_fraction_leaf
0.0
6902
n_estimators
10
6902
n_jobs
1
6902
oob_score
false
6902
random_state
1
6902
verbose
0
6902
warm_start
false
6902
axis
0
6947
categorical_features
[]
6947
copy
true
6947
fill_empty
0
6947
missing_values
"NaN"
6947
strategy
"median"
6947
strategy_nominal
"most_frequent"
6947
verbose
0
6947
categorical_features
[]
6948
dtype
{"oml-python:serialized_object": "type", "value": "np.float64"}
6948
handle_unknown
"ignore"
6948
n_values
"auto"
6948
sparse
true
6948
threshold
0.0
6949
cv
null
7096
error_score
"raise"
7096
fit_params
{}
7096
iid
true
7096
n_iter
50
7096
n_jobs
1
7096
param_distributions
{"classifier__max_features": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], "classifier__bootstrap": [true, false], "classifier__min_samples_split": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], "classifier__criterion": ["gini", "entropy"], "imputation__strategy": ["mean", "median", "most_frequent"]}
7096
pre_dispatch
"2*n_jobs"
7096
random_state
1
7096
refit
true
7096
return_train_score
true
7096
scoring
null
7096
verbose
0
7096
openml-python
Sklearn_0.18.1.
1491
one-hundred-plants-margin
https://www.openml.org/data/download/1592283/phpCsX3fx
-1
13193286
description
https://api.openml.org/data/download/13193286/description.xml
-1
13193287
predictions
https://api.openml.org/data/download/13193287/predictions.arff
-1
13193288
trace
https://api.openml.org/data/download/13193288/trace.arff
area_under_roc_curve
0.9899674479166669 [0.949574,0.998363,0.999605,0.999961,0.998895,0.990017,0.997593,0.963088,0.995778,0.99854,0.995699,0.998224,0.996528,0.981909,0.99925,0.977588,0.999566,0.985361,0.981435,0.959695,0.99712,0.991655,0.981751,1,0.994121,0.934521,0.980015,0.995778,0.991221,0.999092,0.999961,1,0.995699,0.990037,0.998461,0.99858,0.991951,0.982974,0.998501,0.999527,0.996291,0.997751,0.999645,0.99562,0.996291,0.989879,0.998106,0.993726,0.995462,0.993351,0.990649,0.982461,0.9811,0.982008,0.99349,0.980646,1,0.999842,0.991675,0.984967,0.999684,0.996725,0.959852,0.979384,0.998106,0.992326,0.974254,1,0.986348,0.985322,0.98548,0.997554,0.982106,0.988676,0.968119,0.989978,0.998698,0.961628,0.999842,0.999014,0.985894,0.993884,0.998146,0.99345,0.982678,0.995739,0.999842,0.986308,0.996607,0.998422,0.995975,0.999171,0.992188,0.985006,0.996212,0.999527,0.990294,0.989583,0.950797,0.993332]
average_cost
0
f_measure
0.6816827331100107 [0.384615,0.857143,0.909091,0.967742,0.833333,0.375,0.903226,0.777778,0.580645,0.933333,0.764706,0.875,0.787879,0.5,0.941176,0.787879,0.833333,0.4375,0.545455,0.307692,0.810811,0.933333,0.642857,1,0.619048,0.153846,0.375,0.722222,0.903226,0.9375,0.909091,0.967742,0.882353,0.5,0.717949,0.717949,0.242424,0.235294,0.882353,0.882353,0.827586,0.764706,0.864865,0.611111,0.764706,0.666667,0.83871,0.827586,0.75,0.777778,0.606061,0.642857,0.709677,0.4,0.666667,0.296296,0.941176,0.842105,0.451613,0.538462,0.909091,0.756757,0.5625,0.545455,0.681818,0.606061,0.322581,0.914286,0.26087,0.466667,0.709677,0.705882,0.352941,0.8,0.363636,0.709677,0.888889,0.105263,0.914286,0.903226,0.434783,0.75,0.774194,0.545455,0.578947,0.875,0.967742,0.5,0.727273,0.9375,0.705882,0.833333,0.666667,0.461538,0.727273,0.833333,0.722222,0.592593,0.347826,0.580645]
kappa
0.6919191919191918
kb_relative_information_score
1094.593270607665
mean_absolute_error
0.014027697957893976
mean_prior_absolute_error
0.019800000000000036
number_of_instances
1600 [16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16]
precision
0.6897121729431932 [0.5,0.789474,0.882353,1,0.75,0.375,0.933333,0.7,0.6,1,0.722222,0.875,0.764706,0.583333,0.888889,0.764706,0.75,0.4375,0.529412,0.4,0.714286,1,0.75,1,0.5,0.2,0.375,0.65,0.933333,0.9375,0.882353,1,0.833333,0.5,0.608696,0.608696,0.235294,0.222222,0.833333,0.833333,0.923077,0.722222,0.761905,0.55,0.722222,0.714286,0.866667,0.923077,0.75,0.7,0.588235,0.75,0.733333,0.555556,0.818182,0.363636,0.888889,0.727273,0.466667,0.7,0.882353,0.666667,0.5625,0.529412,0.535714,0.588235,0.333333,0.842105,0.428571,0.5,0.733333,0.666667,0.333333,0.736842,0.666667,0.733333,0.8,0.333333,0.842105,0.933333,0.714286,0.75,0.8,0.529412,0.5,0.875,1,0.583333,0.705882,0.9375,0.666667,0.75,0.818182,0.6,0.705882,0.75,0.65,0.727273,0.571429,0.6]
predictive_accuracy
0.695
prior_entropy
6.6438561897747395
recall
0.695 [0.3125,0.9375,0.9375,0.9375,0.9375,0.375,0.875,0.875,0.5625,0.875,0.8125,0.875,0.8125,0.4375,1,0.8125,0.9375,0.4375,0.5625,0.25,0.9375,0.875,0.5625,1,0.8125,0.125,0.375,0.8125,0.875,0.9375,0.9375,0.9375,0.9375,0.5,0.875,0.875,0.25,0.25,0.9375,0.9375,0.75,0.8125,1,0.6875,0.8125,0.625,0.8125,0.75,0.75,0.875,0.625,0.5625,0.6875,0.3125,0.5625,0.25,1,1,0.4375,0.4375,0.9375,0.875,0.5625,0.5625,0.9375,0.625,0.3125,1,0.1875,0.4375,0.6875,0.75,0.375,0.875,0.25,0.6875,1,0.0625,1,0.875,0.3125,0.75,0.75,0.5625,0.6875,0.875,0.9375,0.4375,0.75,0.9375,0.75,0.9375,0.5625,0.375,0.75,0.9375,0.8125,0.5,0.25,0.5625]
relative_absolute_error
0.7084695938330278
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.07689637827813983
root_relative_squared_error
0.7728376802600753
total_cost
0
area_under_roc_curve
0.9916035994347588 [0.955975,1,1,1,0.993671,1,1,1,0.993671,1,0.981013,1,0.996835,0.993671,1,1,1,0.990506,1,0.987421,1,1,0.993711,1,0.993711,0.987342,0.955696,0.996835,0.977848,1,1,1,1,1,1,0.993711,0.993711,0.993711,0.993711,1,1,0.993711,0.996835,0.996835,1,0.974684,1,1,1,0.973101,0.990506,1,1,1,1,1,1,1,0.968553,1,1,1,0.996835,0.993671,1,1,0.96519,1,1,0.91195,0.993671,1,0.987342,1,1,1,1,0.974684,1,1,0.987342,1,0.987421,1,0.968354,0.981013,1,1,0.996835,1,0.990506,1,1,0.971519,1,0.996835,0.993711,0.996835,0.848101,1]
area_under_roc_curve
0.9936390414775895 [0.943396,1,1,1,1,1,0.993671,1,1,1,0.993671,1,0.996835,0.952532,1,1,1,0.993671,0.981013,0.993711,1,0.937107,1,1,1,0.946203,0.996835,1,1,1,1,1,1,0.993671,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.996835,0.96519,1,0.984177,1,0.955975,1,1,1,0.977848,1,1,1,1,1,0.993671,0.993671,1,1,0.987421,0.993671,1,0.990506,1,1,1,1,0.990506,1,0.984177,0.96519,0.996835,1,1,1,1,1,0.990506,1,1,0.987342,1,0.996835,0.984177,0.996835,1,0.993711,0.981013,0.974684,1]
area_under_roc_curve
0.9914285785367406 [1,1,1,1,1,0.984177,1,1,0.987342,1,1,1,1,0.968354,0.993671,0.977848,1,0.977848,0.974843,0.920886,1,1,0.990506,1,1,0.987342,0.996835,0.987421,1,1,1,1,0.977848,0.977848,1,1,0.993711,0.949686,1,1,0.996835,1,1,1,0.993671,0.981132,0.987342,1,0.981013,1,0.977848,0.993711,1,0.993671,1,0.981013,1,1,0.943396,0.984177,1,0.993711,1,0.987421,1,0.990506,1,1,0.993711,0.984177,1,1,0.974843,1,0.993711,1,1,0.933544,1,1,0.981132,1,0.996835,1,1,1,1,1,0.993671,1,1,1,1,0.949367,1,1,0.996835,0.990506,0.937107,1]
area_under_roc_curve
0.990206044502826 [0.990506,0.988924,0.993671,1,0.996835,0.990506,1,1,0.996835,1,0.996835,1,1,0.996835,1,1,1,1,0.830189,0.920886,1,1,1,1,0.949686,0.952532,0.962025,1,1,1,1,1,1,0.990506,1,1,1,0.981132,1,1,0.996835,1,1,1,1,1,1,1,0.996835,1,0.996835,1,1,0.968354,1,0.990506,1,1,1,0.977848,1,1,0.996835,1,0.996835,0.990506,0.949367,1,1,0.996835,0.966772,1,0.943396,0.993671,1,0.987342,1,0.911392,1,1,0.949686,0.996835,0.993671,0.981013,1,1,1,1,1,1,1,1,0.955975,1,1,1,0.987342,0.993671,0.981132,0.974684]
area_under_roc_curve
0.9897506418676855 [1,1,1,1,1,1,0.987421,0.996835,1,1,1,0.993671,1,0.893082,1,0.93038,1,0.990506,1,0.987342,0.984177,1,0.993671,1,0.996835,0.987342,0.968354,1,1,0.990506,1,1,1,0.976266,1,1,1,0.981013,1,1,0.993671,0.996835,1,1,1,1,1,0.996835,1,1,0.993711,0.968553,0.898734,0.981132,0.943038,0.996835,1,0.993671,0.993671,0.962264,1,1,0.993711,1,1,1,0.943396,1,0.974684,0.987342,1,1,0.981132,1,0.924051,1,0.993671,0.968354,1,1,1,1,1,0.987342,1,0.974843,1,0.993711,1,1,1,0.996835,1,0.987421,0.974684,1,0.996835,1,0.943396,0.984177]
area_under_roc_curve
0.9864206422657429 [0.678797,1,1,1,0.993711,0.984177,1,1,1,0.993671,0.996835,1,0.987421,1,1,1,1,1,0.993711,0.968354,1,0.96519,0.870253,1,0.993671,0.977848,0.981013,1,1,1,1,1,0.990506,0.996835,1,0.996835,0.987342,0.968354,1,1,1,0.996835,1,1,1,1,1,0.977848,0.996835,0.981013,0.981132,0.993711,1,1,0.993671,0.984177,1,1,0.993671,1,1,0.996835,0.987421,1,0.993671,1,0.987421,1,0.968354,1,0.949367,0.990506,0.993711,1,0.892405,1,0.996835,0.987342,1,1,0.993711,0.993671,1,0.990506,0.974843,1,1,0.981132,0.987342,1,1,1,0.993711,1,1,1,1,0.968553,0.955975,1]
area_under_roc_curve
0.9898637150704561 [0.993671,1,1,1,1,1,1,1,0.993711,1,1,1,0.987342,1,1,0.990506,1,0.993711,0.996835,0.946203,1,1,0.993671,1,0.993671,0.408805,0.987421,0.974684,1,1,1,1,1,1,1,1,0.996835,0.990506,1,0.993711,1,1,1,1,0.981132,1,1,0.984177,1,1,0.987421,0.984177,1,1,0.990506,0.946203,1,1,0.990506,0.968553,1,0.993671,1,0.920886,1,0.993711,0.987421,1,0.993671,0.990506,0.993711,1,1,1,0.996835,1,1,0.974843,1,1,0.990506,1,0.993671,0.993671,0.996835,1,1,0.981132,1,1,1,1,0.981013,1,1,1,1,1,0.955696,1]
area_under_roc_curve
0.9892454820476081 [1,1,1,1,1,0.981013,0.996835,1,1,1,0.993711,1,0.996835,1,1,0.939873,1,0.949686,0.984177,0.927215,0.996835,1,1,1,1,0.981132,0.981132,1,0.899371,1,1,1,1,1,0.996835,1,0.962025,0.981013,1,1,1,1,1,0.996835,0.987421,1,1,1,0.993711,1,0.993711,1,0.949367,1,1,0.993671,1,1,0.993671,1,1,0.987342,1,0.96519,1,0.949686,0.918239,1,0.981013,0.974684,1,1,0.990506,0.915094,0.996835,1,1,0.91195,1,1,0.996835,0.993711,0.996835,0.996835,0.93038,0.996835,1,0.943396,1,1,1,1,1,0.974843,0.993711,1,0.946203,0.993711,0.996835,1]
area_under_roc_curve
0.9928882055568824 [0.981132,0.993671,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,0.981132,1,1,1,1,0.974843,1,1,0.962264,0.993711,0.996835,0.996835,1,1,1,1,1,1,1,0.996835,0.971519,1,1,0.987421,0.990506,1,0.971519,0.993671,0.936709,1,1,0.993711,1,1,0.987342,1,0.946203,1,0.974843,1,1,0.996835,0.984177,1,1,0.996835,0.993671,1,1,1,1,0.984177,0.993711,1,0.987421,0.987342,1,0.962025,0.949367,1,1,1,1,0.987342,1,1,0.981132,0.993671,1,1,0.996835,0.987421,1,1,0.996835,0.996835,0.981013,0.993711,1,0.993711,1,0.952532,1]
area_under_roc_curve
0.9880647440490403 [1,1,1,1,1,1,1,0.424528,0.993671,1,0.987421,1,1,0.993671,0.996835,1,1,0.968553,0.993671,0.987421,1,1,1,1,0.993671,0.974843,1,1,1,1,1,1,1,0.981132,1,1,0.996835,0.996835,0.996835,1,1,0.996835,1,0.993671,0.996835,0.992089,1,0.996835,1,1,1,0.939873,1,0.984177,1,1,1,1,0.993671,1,1,1,0.721519,0.981013,0.987421,0.993711,0.976266,1,1,0.993711,1,1,0.977848,1,1,1,0.993711,1,0.993711,1,1,0.949686,1,0.987421,1,1,0.996835,0.936709,1,0.993671,0.993671,1,1,1,1,1,1,1,1,1]
average_cost
0
average_cost
0
average_cost
0
average_cost
0
average_cost
0
average_cost
0
average_cost
0
average_cost
0
average_cost
0
average_cost
0
kappa
0.7347124077217639
kappa
0.6905344596194837
kappa
0.715752072641137
kappa
0.6968141802534443
kappa
0.66525875340465
kappa
0.658875552747947
kappa
0.7030484915495183
kappa
0.6652323240298448
kappa
0.684156500454025
kappa
0.7031305514981644
kb_relative_information_score
107.4790369606146
kb_relative_information_score
108.03855753432389
kb_relative_information_score
106.53640231213863
kb_relative_information_score
110.33203741179508
kb_relative_information_score
105.84859830240286
kb_relative_information_score
109.01352295717896
kb_relative_information_score
112.75176075724441
kb_relative_information_score
109.03929443622437
kb_relative_information_score
113.90178808524453
kb_relative_information_score
111.65227185049673
mean_absolute_error
0.014414874142401593
mean_absolute_error
0.014365576603747283
mean_absolute_error
0.014528431968304098
mean_absolute_error
0.013860869418571628
mean_absolute_error
0.014605309909818332
mean_absolute_error
0.01399238515875453
mean_absolute_error
0.013512044791400887
mean_absolute_error
0.01391684386487605
mean_absolute_error
0.013306371947088258
mean_absolute_error
0.013774271773977085
mean_prior_absolute_error
0.019800000000000033
mean_prior_absolute_error
0.019800000000000033
mean_prior_absolute_error
0.019800000000000033
mean_prior_absolute_error
0.019800000000000033
mean_prior_absolute_error
0.019800000000000033
mean_prior_absolute_error
0.019800000000000033
mean_prior_absolute_error
0.019800000000000033
mean_prior_absolute_error
0.019800000000000033
mean_prior_absolute_error
0.019800000000000033
mean_prior_absolute_error
0.019800000000000033
number_of_instances
160 [1,2,2,1,2,1,2,1,2,1,2,2,2,2,2,1,2,2,2,1,1,1,1,1,1,2,2,2,2,1,2,2,2,2,2,1,1,1,1,2,1,1,2,2,2,2,1,1,2,2,2,2,1,2,1,1,2,1,1,2,1,1,2,2,1,2,2,1,1,1,2,1,2,2,1,2,2,2,2,2,2,2,1,1,2,2,2,2,2,1,2,1,2,2,2,2,1,2,2,1]
number_of_instances
160 [1,2,2,1,2,1,2,1,2,1,2,2,2,2,2,1,2,2,2,1,1,1,1,1,1,2,2,2,2,1,2,2,2,2,2,1,1,1,1,2,1,1,2,2,2,2,1,1,2,2,2,2,1,2,1,1,2,1,1,2,1,1,2,2,1,2,2,1,1,1,2,1,2,2,1,2,2,2,2,2,2,2,1,1,2,2,2,2,2,1,2,1,2,2,2,2,1,2,2,1]
number_of_instances
160 [2,2,2,1,2,2,1,2,2,2,2,2,1,2,2,2,2,2,1,2,1,1,2,1,1,2,2,1,2,2,1,2,2,2,1,1,1,1,1,2,2,1,1,1,2,1,2,1,2,2,2,1,1,2,1,2,1,2,1,2,1,1,2,1,2,2,2,2,1,2,2,2,1,2,1,2,2,2,2,2,1,2,2,2,1,1,1,2,2,1,2,1,1,2,2,1,2,2,1,2]
number_of_instances
160 [2,2,2,1,2,2,1,2,2,2,2,2,1,2,2,2,2,2,1,2,1,1,2,1,1,2,2,1,2,2,1,2,2,2,1,1,1,1,1,2,2,1,1,1,2,1,2,1,2,2,2,1,1,2,1,2,1,2,1,2,1,1,2,1,2,2,2,2,1,2,2,2,1,2,1,2,2,2,2,2,1,2,2,2,1,1,1,2,2,1,2,1,1,2,2,1,2,2,1,2]
number_of_instances
160 [2,1,2,2,1,2,1,2,1,2,2,2,1,1,1,2,2,2,1,2,2,2,2,2,2,2,2,1,1,2,1,1,2,2,1,2,2,2,2,2,2,2,1,1,1,1,2,2,2,2,1,1,2,1,2,2,1,2,2,1,2,2,1,1,2,2,1,2,2,2,2,2,1,1,2,1,2,2,2,1,1,2,2,2,1,1,1,1,2,2,1,2,1,1,2,1,2,1,1,2]
number_of_instances
160 [2,1,2,2,1,2,1,2,1,2,2,2,1,1,1,2,2,2,1,2,2,2,2,2,2,2,2,1,1,2,1,1,2,2,1,2,2,2,2,2,2,2,1,1,1,1,2,2,2,2,1,1,2,1,2,2,1,2,2,1,2,2,1,1,2,2,1,2,2,2,2,2,1,1,2,1,2,2,2,1,1,2,2,2,1,1,1,1,2,2,1,2,1,1,2,1,2,1,1,2]
number_of_instances
160 [2,1,1,2,1,2,2,2,1,2,1,1,2,1,1,2,1,1,2,2,2,2,2,2,2,1,1,2,1,2,2,1,1,1,2,2,2,2,2,1,2,2,2,2,1,2,2,2,1,1,1,2,2,1,2,2,2,2,2,1,2,2,1,2,2,1,1,2,2,2,1,2,2,1,2,1,1,1,1,1,2,1,2,2,2,2,2,1,1,2,1,2,2,1,1,2,2,1,2,2]
number_of_instances
160 [2,1,1,2,1,2,2,2,1,2,1,1,2,1,1,2,1,1,2,2,2,2,2,2,2,1,1,2,1,2,2,1,1,1,2,2,2,2,2,1,2,2,2,2,1,2,2,2,1,1,1,2,2,1,2,2,2,2,2,1,2,2,1,2,2,1,1,2,2,2,1,2,2,1,2,1,1,1,1,1,2,1,2,2,2,2,2,1,1,2,1,2,2,1,1,2,2,1,2,2]
number_of_instances
160 [1,2,1,2,2,1,2,1,2,1,1,1,2,2,2,1,1,1,2,1,2,2,1,2,2,1,1,2,2,1,2,2,1,1,2,2,2,2,2,1,1,2,2,2,2,2,1,2,1,1,2,2,2,2,2,1,2,1,2,2,2,2,2,2,1,1,2,1,2,1,1,1,2,2,2,2,1,1,1,2,2,1,1,1,2,2,2,2,1,2,2,2,2,2,1,2,1,2,2,1]
number_of_instances
160 [1,2,1,2,2,1,2,1,2,1,1,1,2,2,2,1,1,1,2,1,2,2,1,2,2,1,1,2,2,1,2,2,1,1,2,2,2,2,2,1,1,2,2,2,2,2,1,2,1,1,2,2,2,2,2,1,2,1,2,2,2,2,2,2,1,1,2,1,2,1,1,1,2,2,2,2,1,1,1,2,2,1,1,1,2,2,2,2,1,2,2,2,2,2,1,2,1,2,2,1]
predictive_accuracy
0.7375
predictive_accuracy
0.69375
predictive_accuracy
0.71875
predictive_accuracy
0.7
predictive_accuracy
0.66875
predictive_accuracy
0.6625
predictive_accuracy
0.70625
predictive_accuracy
0.66875
predictive_accuracy
0.6875
predictive_accuracy
0.70625
prior_entropy
6.6438561897747395
prior_entropy
6.6438561897747395
prior_entropy
6.6438561897747395
prior_entropy
6.6438561897747395
prior_entropy
6.6438561897747395
prior_entropy
6.6438561897747395
prior_entropy
6.6438561897747395
prior_entropy
6.6438561897747395
prior_entropy
6.6438561897747395
prior_entropy
6.6438561897747395
recall
0.7375 [0,1,1,1,0.5,1,1,1,0,1,0.5,1,1,0.5,1,1,1,0,1,0,1,1,0,1,0,0.5,0,1,0.5,1,1,1,1,0,0,1,0,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,0,1,1,1,0.5,0.5,1,0.5,0.5,1,0,0,1,1,0.5,1,1,1,1,0,1,1,0.5,1,0,1,0.5,0.5,1,1,1,1,0.5,1,1,0.5,1,1,1,1,0,1]
recall
0.69375 [0,1,1,1,1,1,0.5,1,0.5,1,1,1,0.5,0.5,1,1,0.5,0.5,0.5,1,1,0,1,1,1,0,0.5,1,1,1,1,1,1,0.5,1,1,0,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1,0,1,0,1,1,1,0,1,1,1,0.5,1,0.5,0,1,0,0,1,1,0.5,1,0,0,1,0,1,0,0.5,1,1,1,1,1,1,0.5,0.5,1,0.5,1,0.5,0.5,0.5,1,1,0,0,0]
recall
0.71875 [0.5,0.5,1,1,1,0,1,1,0.5,1,1,0.5,1,0.5,1,0.5,1,0.5,0,0,1,1,0.5,1,1,0,1,0,1,1,1,1,0.5,0.5,1,1,0,0,1,1,0.5,1,1,1,1,0,0.5,1,0.5,1,0.5,1,0,0.5,1,0,1,1,0,0.5,1,1,0.5,1,1,1,0.5,1,0,1,1,1,0,0.5,0,1,1,0,1,1,0,1,1,0.5,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0.5,0,1]
recall
0.7 [0,1,0.5,1,1,0,1,1,0.5,0.5,1,1,1,0.5,1,1,1,0.5,0,0,1,1,0.5,1,0,0,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,0.5,1,0.5,1,1,1,0,1,1,1,1,1,0.5,0,1,0,0.5,0,0.5,0,1,1,0.5,1,0,1,1,0,0.5,1,0.5,1,1,1,1,1,1,1,1,0,0,0.5,1,0.5,0.5,0,0.5]
recall
0.66875 [1,1,1,1,1,0.5,0,1,0,1,1,0.5,1,0,1,0.5,1,0.5,1,1,0.5,1,0.5,1,0.5,0,0,1,1,0.5,1,0,1,0.5,1,1,0.5,0,1,1,0.5,0.5,1,1,1,1,1,0.5,1,1,0,0,0.5,0,0,0.5,1,1,0.5,0,1,1,1,1,0.5,0.5,0,1,0,0.5,1,1,0,1,0,1,1,0,1,1,1,1,1,0,1,1,1,0,1,1,1,0.5,1,0,0.5,1,0.5,1,0,0]
recall
0.6625 [0,1,1,0.5,1,0.5,1,1,0,0.5,0.5,1,0,1,1,1,1,1,0,0,1,0.5,0.5,1,1,0.5,0,1,1,1,1,1,1,1,1,0.5,0.5,0,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,0,1,1,0,0,0,1,1,0,1,1,1,0,0,1,1,1,1,0,0.5,0.5,0,0,1,0.5,1,1,0,1,1,0,0.5,1,0.5,0,1,1,0,0.5,1,1,1,0,1,0.5,1,1,0,0,0.5]
recall
0.70625 [0.5,1,1,1,1,0,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,0.5,1,1,0,0,0.5,1,1,1,1,1,1,1,0.5,0,0.5,1,0,0.5,0.5,1,1,0,0.5,0.5,0.5,1,1,0,0.5,1,0,1,0,1,1,0,0,0.5,0.5,1,0,1,1,0,1,0.5,0.5,1,1,1,1,0.5,1,1,0,1,1,0.5,1,0.5,1,1,1,1,0,0,1,1,1,0,0,1,0.5,1,0,0.5,1]
recall
0.66875 [0,1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,0,1,0.5,1,0,0.5,0,1,1,1,1,1,0,1,1,0,1,0.5,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,0.5,0.5,1,1,1,1,1,0.5,0,0.5,1,0,0,1,0,0,0,1,0.5,0,0,1,1,0,1,1,0,0,0,1,0,0.5,1,0,1,1,0,1,1,0,1,1,0.5,1,0,0.5]
recall
0.6875 [0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,0,1,1,0,1,1,0,0,0.5,1,1,1,1,1,0,1,1,0.5,0,1,1,0,0.5,1,0.5,0.5,0,1,0.5,0,1,1,0,1,0,0.5,0,1,1,0.5,0.5,1,1,0.5,1,1,0,1,1,0.5,0,0,0,0,1,0,0,1,1,1,1,0,1,1,0,0.5,1,1,0.5,0,1,1,1,0,0.5,1,1,1,1,0.5,1]
recall
0.70625 [1,1,1,1,1,0,1,0,1,1,0,1,1,0.5,1,1,1,0,0,0,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1,1,0,0.5,0.5,1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,0.5,0.5,0,0.5,0,1,1,0.5,0.5,1,1,0,0.5,1,1,0,1,0.5,1,1,1,0.5,1,0,1,1,0,1,1,0.5,0,1,0,1,1,0.5,0,1,0.5,0.5,1,1,0.5,1,1,1,0,1,0]
relative_absolute_error
0.7280239465859378
relative_absolute_error
0.7255341719064272
relative_absolute_error
0.7337591903183877
relative_absolute_error
0.7000439100288689
relative_absolute_error
0.7376419146372882
relative_absolute_error
0.7066861191290156
relative_absolute_error
0.6824265046162052
relative_absolute_error
0.702870902266466
relative_absolute_error
0.6720389872266785
relative_absolute_error
0.6956702916150032
root_mean_prior_squared_error
0.09949874371066206
root_mean_prior_squared_error
0.09949874371066206
root_mean_prior_squared_error
0.09949874371066206
root_mean_prior_squared_error
0.09949874371066206
root_mean_prior_squared_error
0.09949874371066206
root_mean_prior_squared_error
0.09949874371066206
root_mean_prior_squared_error
0.09949874371066206
root_mean_prior_squared_error
0.09949874371066206
root_mean_prior_squared_error
0.09949874371066206
root_mean_prior_squared_error
0.09949874371066206
root_mean_squared_error
0.07805923487772606
root_mean_squared_error
0.07773050994243409
root_mean_squared_error
0.07870642969467044
root_mean_squared_error
0.07643585097302165
root_mean_squared_error
0.07887038506224886
root_mean_squared_error
0.0771510582523066
root_mean_squared_error
0.07515105235615725
root_mean_squared_error
0.07663755308223331
root_mean_squared_error
0.07411590703272239
root_mean_squared_error
0.07596792672448988
root_relative_squared_error
0.7845248288231543
root_relative_squared_error
0.7812210189152837
root_relative_squared_error
0.791029381471843
root_relative_squared_error
0.7682092066940436
root_relative_squared_error
0.7926771949161534
root_relative_squared_error
0.7753973103083439
root_relative_squared_error
0.755296494744629
root_relative_squared_error
0.7702363891657962
root_relative_squared_error
0.7448928928012214
root_relative_squared_error
0.7635063910495318
total_cost
0
total_cost
0
total_cost
0
total_cost
0
total_cost
0
total_cost
0
total_cost
0
total_cost
0
total_cost
0
total_cost
0
usercpu_time_millis
44418.09254300097
usercpu_time_millis
44478.21425499933
usercpu_time_millis
44336.530954999034
usercpu_time_millis
44793.38196699973
usercpu_time_millis
44344.641107003554
usercpu_time_millis
44409.81389600347
usercpu_time_millis
45482.90044400346
usercpu_time_millis
44776.35778200056
usercpu_time_millis
44366.8136319975
usercpu_time_millis
44516.532621000806
usercpu_time_millis_testing
4.040298001200426
usercpu_time_millis_testing
4.034353998576989
usercpu_time_millis_testing
4.128215998207452
usercpu_time_millis_testing
4.129708999244031
usercpu_time_millis_testing
4.050963001645869
usercpu_time_millis_testing
4.0022780012805015
usercpu_time_millis_testing
4.074653003044659
usercpu_time_millis_testing
4.032277000078466
usercpu_time_millis_testing
4.040651998366229
usercpu_time_millis_testing
4.045940000651171
usercpu_time_millis_training
44414.052244999766
usercpu_time_millis_training
44474.17990100075
usercpu_time_millis_training
44332.40273900083
usercpu_time_millis_training
44789.25225800049
usercpu_time_millis_training
44340.59014400191
usercpu_time_millis_training
44405.81161800219
usercpu_time_millis_training
45478.82579100042
usercpu_time_millis_training
44772.32550500048
usercpu_time_millis_training
44362.772979999136
usercpu_time_millis_training
44512.486681000155